FinSight AI: Analyze Financials Fast

Key insights

• Custom GPT: Build a tailored assistant to parse a company’s balance sheet, edit its instructions, and publish it for repeated use.
  It extracts line items and key ratios so analysts avoid manual copying and formatting.
• Multi-statement analysis: Configure the tool to read income statements, balance sheets, and cash flow statements together.
  It can compare companies and trends across time to highlight material changes.
• Focused agents: Limit the GPT to only perform financial-statement work so it stays precise and reliable.
  Set clear instructions and constraints to keep outputs on-topic and consistent.
• Visuals: Add charts and tables to make findings easier to read and share.
  Visual outputs speed decision-making and support quick reviews in meetings or reports.
• Financial reports analysis model: Microsoft’s fine-tuned model handles tables, formulas, and long documents with a very large context window (up to 128K token context).
  It processes filings and long reports without losing earlier data in the document.
• Automation: Integrate the GPT into productivity tools to automate extraction, summarization, and routine variance checks.
  This reduces manual work, lowers errors, and surfaces actionable insights faster for analysts and managers.

Kenji Farré (Kenji Explains) [MVP] released a hands-on YouTube video that shows how to build a custom GPT to analyze financial statements in seconds. In clear steps, he demonstrates setting up a model to parse a company's balance sheet, then expands the tool to handle multiple financial statements like the income statement. Consequently, the video serves both beginners and experienced analysts by combining practical tutorials with commentary on how to shape AI behavior for finance tasks.

Setting Up a Custom GPT for Balance Sheet Analysis

First, Kenji walks viewers through creating a basic custom GPT that focuses on one document type, the balance sheet. He uses Nike as a practice example to show how the GPT extracts numbers, highlights key ratios, and explains changes between periods in plain terms. As a result, users can replicate the process to automate what used to be manual, repetitive work.

Next, he explains how to edit the GPT's instructions so the tool prioritizes relevant line items and avoids irrelevant commentary. This step is important because subtle changes in prompts produce very different outputs, and Kenji emphasizes testing and iteration. Moreover, he shows how to publish the GPT so colleagues can reuse it, which improves team efficiency but also raises questions about version control and review processes.

Expanding to Multiple Statements and Real Companies

After the initial setup, Kenji broadens the scope to include the income statement and combined analysis across multiple filings, using companies such as PepsiCo and Coca‑Cola. He demonstrates how a single GPT can compare trends, compute margins, and flag inconsistencies across statements, thereby accelerating cross-company comparisons. Consequently, analysts can shift from data collection to interpretation more quickly than before.

However, Kenji also points out tradeoffs when increasing scope: broader analysis requires careful prompt constraints to prevent off-topic responses and to keep computations focused. In addition, pulling together different statements increases complexity in mapping accounts and normalizing formats. Therefore, teams must balance convenience against the need for preprocessing and quality checks.

Constraining Behavior and Adding Visuals

Importantly, the video covers techniques to limit the GPT so it strictly concentrates on financial statement analysis rather than wandering into unrelated content. Kenji suggests clear instruction layers and example-based guidance to reduce hallucinations and improve consistency. He further demonstrates how to embed visuals, such as trend charts or highlighted table excerpts, to make outputs more actionable for presentations or decision-making.

While visuals increase clarity, Kenji warns that they bring design and integration challenges, including matching data sources and ensuring visuals update correctly. Additionally, adding charts often requires extra steps in the workflow or integration with spreadsheet tools, so teams must weigh the benefits of richer outputs against added maintenance. Consequently, organizations should plan for both technical and human review when deploying visual analyses.

Microsoft Technology and Enterprise Implications

The video contextualizes the approach within a broader enterprise landscape and references a specialized financial analysis model from Microsoft that has been tuned for long documents. This model, described as a decoder-only transformer with around 7 billion parameters and long context lengths, can process extensive filings such as SEC reports without losing detail. In practice, that capability enables faster extraction of tables and complex formulas, which supports deeper automated reasoning across large documents.

Yet, deploying such powerful tools introduces tradeoffs in cost, privacy, and governance. For example, integrating models via platforms like Azure AI Foundry or building agents in Copilot Studio and Microsoft 365 Copilot can speed implementation, but they also require careful security reviews and oversight. Therefore, firms must balance the efficiency gains against the need for access controls, audit logs, and regulatory compliance.

Practical Challenges and Final Takeaways

Kenji’s tutorial underscores both the promise and the limits of automating financial analysis. On one hand, a well-designed custom GPT can dramatically reduce the time analysts spend extracting and summarizing figures, letting them focus on insight and strategy. On the other hand, the approach depends on clean inputs, precise instructions, and ongoing validation to avoid misleading outputs.

Ultimately, the video offers a practical blueprint for teams considering AI-assisted finance workflows: start small with focused tasks, iterate on prompts, and add guardrails and visuals gradually. By doing so, organizations can capture immediate efficiencies while preparing for the operational and governance work that follows larger-scale adoption.

Keywords

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